Abstract The injection of CO 2 into geological reservoirs can acidify the reservoir environment, leading to the dissolution of mineral grains and increasing the risk of CO 2 leakage. A deeper understanding of these micro‐scale dissolution mechanisms is therefore essential for the safety of geological CO 2 sequestration. To address this challenge, we develop a multi‐field coupled simulation framework that integrates the Lattice Boltzmann Method (LBM) with the Finite‐Discrete Element Method (FDEM) to model solute transport and mineral micro‐structure dissolution. The framework incorporates a geometric mapping detection scheme and an improved momentum exchange algorithm to capture fluid‐solid interactions. Furthermore, a novel updating scheme for dissolution rates and solute concentrations is proposed, explicitly accounting for mass conservation and complex mineral morphology. A dynamic geometric topology updating algorithm is also proposed to track the morphological evolution of mineral grains. The accuracy and capability of the LBM‐FDEM framework are validated through several numerical benchmarks, including particle settling, solute release from a moving particle, dissolution of polymorphic particles, and reactive transport within a non‐uniform channel, yielding excellent agreement with existing solutions. Finally, this proposed method is applied to investigate the spatiotemporal evolution of fracture dissolution under varying Péclet numbers. The numerical results reveal that under diffusion‐dominated low Péclet regimes, product accumulation restricts dissolution to the upstream inlet, yielding a typical face dissolution pattern. Conversely, strong advection under high Péclet conditions drives rapid reactant penetration, triggering a uniform channelized widening along the entire fracture. Ultimately, this framework demonstrates significant potential for investigating complex hydro‐mechanical‐chemical couplings in subsurface engineering.
A rational design for next-generation thermo-responsive nanocarriers is proposed, in which polymer chemistry, nanoparticle structure, experimental characterization, and mechanistic modelling are integrated from the earliest stages of material development.
M. Schifone, Giuseppe Nunziata, Filippo Rossi· Advances in Colloid and Inte...· 2 citations
This paper describes the formulation of a numerical model for simulating environmentally driven one-dimensional (1D) ground movements of expansive clay. The formulation is based on a finite-element model that simulates the redistribution of matric suction through a diffusion-type equation, explicitly accounting for volume changes due to wetting and drying of the clay. We synthesize and modify highly nonlinear constitutive relationships for (1) hysteretic soil water retention; (2) reversible soil shrinkage and expansion of clay; and (3) hydraulic conductivity, explicitly incorporating desiccation cracks through a multidomain framework and assuming a critical surface crack depth. These models are well-calibrated to published laboratory tests on a reference expansive clay, Denver bentonite. We demonstrate capabilities of the proposed formulation to simulate the response of a homogeneous expansive clay to periods of drying and wetting, considering the initial matric suction, saturated hydraulic conductivity of the intact clay, and critical crack depth as three primary sources of uncertainty. We compare ensemble model simulations with measured ground movements from an instrumented expansive clay test site in Texas over a 3-year period using detailed records of potential evapotranspiration and precipitation. By assigning weights to the ensemble simulations based on their performance, we constrain the ranges of the three key uncertain parameters. The results showed very reasonable first-order agreement with the measured data and highlight the potential of the proposed formulation. We anticipate that more reliable predictions can be achieved through direct measurements of actual in situ evaporation rates and local soil properties.
Mahdi Seyyedan, Jiali Ma, Ivo Rosa Montenegro et al.· Journal of Geotechnical and...· 1 citation
This paper examines whether differences in the speed with which traded assets respond to a common market shock can predict subsequent relative returns. The framework combines a lagged rolling factor model with Absorption Gap (AG), which measures an asset’s response error, and Shock Coherence (SC), which characterizes the contemporaneous market state. The public specification is evaluated using executable next-open timing, explicit transaction costs, dependence-aware inference, randomized-signal benchmarks, chronological diagnostics, and machine-learning extensions. The study uses 24 ETFs from 4 January 2010 through 28 August 2026, with eight factor proxies excluded from the 16-asset traded cross-section. The corrected public baseline produces a combined Rank IC of -0.00592, an approximately flat zero-cost gross result, and materially negative performance after transaction costs. A within-date randomized-signal benchmark yields an empirical two-sided p-value of 0.299, while standalone Absorption Gap, coherence-conditioned tests, chronological subsamples, and machine-learning models provide no robust evidence of economically viable public alpha. The contribution is therefore methodological as much as empirical: the paper connects an economic hypothesis about heterogeneous information absorption to an executable trading test, documents why the disclosed implementation fails, separates diagnostic and exploratory analysis from confirmatory evidence, and establishes a reproducible public baseline while keeping the proprietary alpha layer outside the evidence package.
Khaybullina Alina· Zenodo (CERN European Organi...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.